Assessment of breast cancer risk among Iraqi women in 2019

Background Breast cancer is one of the most common cancers among women worldwide and the leading cause of death among Iraqi women. Breast cancer cases in Iraq were found to have increased from 26.6/100,000 in 2000 to 31.5/100,000 in 2009. The present study aims to assess the established risk factors of breast cancer among Iraqi women and to highlight strategies that can aid in reducing the incidence. Methods 1093 Iraqi females were enrolled in this cross-sectional study by purposive sampling methods. Data collection occurred from July 2019 to September 2019. 1500 women participated in the study, and 407 women were ultimately excluded. The questionnaire was conducted as a self-administrated form in an online survey. Ethical approval was obtained from the College of Medicine in the University of Baghdad. The Gail Model risk was calculated for each woman by the Breast Cancer Risk Assessment Tool (BCRAT), an interactive model developed by Mitchell Gail that was designed to estimate a woman’s absolute risk of developing breast cancer in the upcoming five years of her life and in her lifetime. Results The ages of the participants ranged from 35 to 84 years old. The mean 5–year risk of breast cancer was found to be 1.3, with 75.3% of women at low risk and 24.7% of women at high risk. The mean lifetime risk of breast cancer was found to be 13.4, with 64.7% of women at low risk, 30.3% at moderate risk, and 5.0% at high risk. The results show that geographically Baghdad presented the highest 5-year risk, followed by Dhi Qar, Maysan, and Nineveh. However, the highest lifetime risk was found in Najaf, followed by Dhi Qar, Baghdad, and Nineveh, successively. Conclusion Breast cancer is a wide-spreading problem in the world and particularly in Iraq, with Gail Model estimations of high risk in several governorates. Prevention programs need to be implemented and awareness campaigns organized in order to highlight the importance of early detection and treatment.

906Can mammographic density add value to the Gail model in risk-stratifying women in BreastScreen Australia?

Background There is significant interest in personalised, risk-based breast cancer screening. This requires high quality risk assessment. The ‘Gail model’ risk assessment tool has been validated on over 40,000 BreastScreen Australia participants. We assess whether adding mammographic density (MD) information improves risk stratification on that cohort. Methods We used questionnaire data, baseline MD readings (using AutoDensity) and linked screening and cancer registry records from 40,158 BreastScreen Australia participants aged 50–69 years (via the lifepool cohort). We investigated incident invasive breast cancer rates by quintiles of Gail model scores, MD, and combinations of Gail and MD. Results Gail scores and MD values were weakly correlated (r≤0.02). Gail and MD were each strong predictors of incident breast cancer, but stronger predictors when used in combination. For example, the odds ratio for incident invasive breast cancer was 3.6 (95%CI 2.5-6.3) for the 17% of women in the upper two quintiles of both Gail and MD scores compared to the 17% of women in the lower two quintiles of both scores. In comparison, the odds ratio for breast cancer between same-size (each 17%) upper and lower groups for Gail score alone was 2.5 (95%CI 1.8-3.4), and for MD 1.9 (95%CI 1.2-2.9). Conclusions Combining Gail and MD categories improves risk stratification on BreastScreen Australia participants, compared to using Gail or MD alone. Key messages While questionnaire data and MD measures are each strong predictors of future invasive breast cancer among BreastScreen Australia participants, risk prediction is stronger when questionnaire and MD measures are combined.

A streamlined model for use in clinical breast cancer risk assessment maintains predictive power and is further improved with inclusion of a polygenic risk score

Five-year absolute breast cancer risk prediction models are required to comply with national guidelines regarding risk reduction regimens. Models including the Gail model are under-utilized in the general population for various reasons, including difficulty in accurately completing some clinical fields. The purpose of this study was to determine if a streamlined risk model could be designed without substantial loss in performance. Only the clinical risk factors that were easily answered by women will be retained and combined with an objective validated polygenic risk score (PRS) to ultimately improve overall compliance with professional recommendations. We first undertook a review of a series of 2,339 Caucasian, African American and Hispanic women from the USA who underwent clinical testing. We first used deidentified test request forms to identify the clinical risk factors that were best answered by women in a clinical setting and then compared the 5-year risks for the full model and the streamlined model in this clinical series. We used OPERA analysis on previously published case-control data from 11,924 Gail model samples to determine clinical risk factors to include in a streamlined model: first degree family history and age that could then be combined with the PRS. Next, to ensure that the addition of PRS to the streamlined model was indeed beneficial, we compared risk stratification using the Streamlined model with and without PRS for the existing case-control datasets comprising 1,313 cases and 10,611 controls of African-American (n = 7421), Caucasian (n = 1155) and Hispanic (n = 3348) women, using the area under the curve to determine model performance. The improvement in risk discrimination from adding the PRS risk score to the Streamlined model was 52%, 46% and 62% for African-American, Caucasian and Hispanic women, respectively, based on changes in log OPERA. There was no statistically significant difference in mean risk scores between the Gail model plus risk PRS compared to the Streamlined model plus PRS. This study demonstrates that validated PRS can be used to streamline a clinical test for primary care practice without diminishing test performance. Importantly, by eliminating risk factors that women find hard to recall or that require obtaining medical records, this model may facilitate increased clinical adoption of 5-year risk breast cancer risk prediction test in keeping with national standards and guidelines for breast cancer risk reduction.

Discriminatory Accuracy of the Gail Model for Breast Cancer Risk Assessment among Iranian Women

Background: The Gail model is the most well-known tool for breast cancer risk assessment worldwide. Although it was validated in various Western populations, inconsistent results were reported from Asian populations. We used data from a large case-control study and evaluated the discriminatory accuracy of the Gail model for breast cancer risk assessment among the Iranian female population. Methods: We used data from 942 breast cancer patients and 975 healthy controls at the Cancer Institute of Iran, Tehran, Iran, in 2016. We refitted the Gail model to our case-control data (the IR-Gail model). We compared the discriminatory power of the IR-Gail with the original Gail model, using ROC curve analyses and estimation of the area under the ROC curve (AUC). Results: Except for the history of biopsies that showed an extremely high relative risk (OR=9.1), the observed ORs were similar to the estimates observed in Gail's study. Incidence rates of breast cancer were extremely lower in Iran than in the USA, leading to a lower average absolute risk among the Iranian population (2.78, ±SD 2.45). The AUC was significantly improved after refitting the model, but it remained modest (0.636 vs. 0.627, ΔAUC = 0.009, bootstrapped P=0.008). We reported that the cut-point of 1.67 suggested in the Gail study did not discriminate between breast cancer patients and controls among the Iranian female population. Conclusion: Although the coefficients from the local study improved the discriminatory accuracy of the model, it remained modest. Cohort studies are warranted to evaluate the validity of the model for Iranian women.

USING GAIL MODEL FOR BREAST CANCER RISK ASSESSMENT OF SAUDI FEMALES LIVING AT NATIONAL GUARD RESIDENTIAL CITY, JEDDAH, SAUDI ARABIA

Background: The primary reason for cancer death in women worldwide is breast cancer. It is also the most prevalent cancer in Saudi Arabia. The risk factors for breast cancer development have been divided into modifiable, which can be prevented, and non modifiable risks. Factors such as menarche at early age and family history of breast cancer are nonmodifiable risks, while lifestyle-related behaviors such as dietary habits, physical activity, smoking, or secondhand smoke are modifiable. Risk assessment tools for breast cancer are used to give patients a degree of their level of risk to better-recommended screening tests. It is also informative for the women about the behaviors they should modify to lower the risk. The Gail Model is the best online available tool to estimate the breast cancer risk for early prevention. Methodology: A cross-sectional survey of 144 Saudi females is conducted. Females aged 35 – 70-year-old, who lives at the National Guard residential city in Jeddah were included. Through home visits, females were interviewed, and an individualized risk assessment was made. Body Breast cancer determinants were collected, and the Mass Index was calculated for each participant. According to the result, specific health education regarding breast cancer prevention and screening was provided for all females who participated in the study. Results: This study revealed that Saudi females have many protective factors against breast cancer, such as multiparity (60%), late age menarche (71%), and breastfeeding (47%). Age and family history (11%) are significant nonmodifiable determinants of breast cancer in our population. On the other hand, other factors related to a sedentary lifestyle such as Obesity (21%) and secondhand smoke (43%) can be modified. Conclusion: Primary prevention of modifiable risk factors is essential for reducing the breast cancer burden. Raising awareness regarding early detection and screening is necessary.